2 citations · 3 across the 9 of their papers we have counts for
10 papers
MRO: Enhancing Reasoning in Diffusion Language Models via Multi-Reward Optimization
Chenglong Wang, Yang Gan, Hang Zhou +10
Recent advances in diffusion language models (DLMs) have presented a promising alternative to traditional autoregressive large language models (LLMs). However, DLMs still lag behin…
Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
Kaiyan Chang, Yonghao Shi, Chenglong Wang +7
Test-Time Scaling (TTS) is a promising approach to progressively elicit the model's intelligence during inference. Recently, training-based TTS methods, such as continued reinforce…
Teaching Language Models to Self-Improve by Learning from Language Feedback
Chi Hu, Yimin Hu, Hang Cao +2
Aligning Large Language Models (LLMs) with human intentions and values is crucial yet challenging. Current methods primarily rely on human preferences, which are costly and insuffi…
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
Chi Hu, Yuan Ge, Xiangnan Ma +5
Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors du…
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
Yuan Ge, Yilun Liu, Chi Hu +10
With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required for training and ev…
Bridging the Granularity Gap for Acoustic Modeling
Chen Xu, Yuhao Zhang, Chengbo Jiao +7
While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies…
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
Chi Hu, Chenglong Wang, Xiangnan Ma +5
This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods require numerous training examples…
The NiuTrans System for WNGT 2020 Efficiency Task
Chi Hu, Bei Li, Ye Lin +5
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models \cite{wang-et…
The NiuTrans System for the WMT21 Efficiency Task
Chenglong Wang, Chi Hu, Yongyu Mu +8
This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt.org/wmt21/efficiency-task.html). Following last year's work, we explore various te…
Learning Architectures from an Extended Search Space for Language Modeling
Yinqiao Li, Chi Hu, Yuhao Zhang +6
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In…